Add BEiT ONNXRT example (#1322)

Signed-off-by: yuwenzho <[email protected]>

Author: yuwenzho

examples/.config/model_params_onnxrt.json

@@ -322,6 +322,13 @@
       "main_script": "main.py",
       "batch_size": 1
     },
+    "beit": {
+      "model_src_dir": "image_recognition/beit/quantization/ptq_static",
+      "dataset_location": "/tf_dataset/pytorch/ImageNet/raw",
+      "input_model": "/tf_dataset2/models/onnx/beit/beit_base_patch16_224_pt22k_ft22kto1k.onnx",
+      "main_script": "main.py",
+      "batch_size": 1
+    },
     "mobilebert_squad_mlperf_qdq": {
       "model_src_dir": "nlp/onnx_model_zoo/mobilebert/quantization/ptq_static",
       "dataset_location": "/tf_dataset2/datasets/squad",

examples/README.md

@@ -1133,6 +1133,12 @@ Intel® Neural Compressor validated examples with multiple compression technique
     <td>Post-Training Static Quantization</td>
     <td><a href="./onnxrt/body_analysis/onnx_model_zoo/arcface/quantization/ptq_static">qlinearops</a></td>
   </tr>
+  <tr>
+    <td>BEiT</td>
+    <td>Image Recognition</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./onnxrt/image_recognition/beit/quantization/ptq_static">qlinearops</a></td>
+  </tr>
   <tr>
     <td>CodeBert</td>
     <td>Natural Language Processing</td>

examples/onnxrt/image_recognition/beit/quantization/ptq_static/README.md

@@ -0,0 +1,55 @@
+Step-by-Step
+============
+
+This example load [BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)(BEiT) model and confirm its accuracy and performance based on [ImageNet-1k dataset](http://www.image-net.org/). You need to download this dataset yourself.
+
+In this example, the BEiT model is pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was first released in [this repository](https://github.com/microsoft/unilm/tree/master/beit). 
+
+
+# Prerequisite
+
+## 1. Environment
+```shell
+pip install neural-compressor
+pip install -r requirements.txt
+```
+> Note: Validated ONNX Runtime [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+## 2. Prepare Model
+
+Prepare DETR R18 model for table structure recognition.
+
+```shell
+python prepare_model.py --input_model=beit_base_patch16_224 --output_model=beit_base_patch16_224_pt22k_ft22kto1k.onnx
+```
+
+## 3. Prepare Dataset
+
+Download and extract [ImageNet-1k](http://www.image-net.org/) to dir: /path/to/imagenet. The dir include below folder:
+
+```bash
+ls /path/to/imagenet
+train  val
+```
+
+# Run
+
+## 1. Quantization
+
+Quantize model with QLinearOps:
+
+```bash
+bash run_quant.sh --input_model=/path/to/model \  # model path as *.onnx
+                   --dataset_location=/path/to/imagenet \
+                   --output_model=/path/to/save \
+                   --quant_format="QOperator"
+```
+
+## 2. Benchmark
+
+```bash
+bash run_benchmark.sh --input_model=/path/to/model \  # model path as *.onnx
+                      --dataset_location=/path/to/imagenet \
+                      --batch_size=batch_size \
+                      --mode=performance # or accuracy
+```